from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A						=			logging.get_logger(__name__)

A						=			{
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		lowerCAmelCase_     =   'table-transformer'
		lowerCAmelCase_     =   ['past_key_values']
		lowerCAmelCase_     =   {
		    'hidden_size': 'd_model',
		    'num_attention_heads': 'encoder_attention_heads',
		}
		def __init__(       self     :     str     ,  snake_case__     :     Tuple=True     ,  snake_case__     :     List[str]=None     ,  snake_case__     :     List[Any]=3     ,  snake_case__     :     List[str]=1_0_0     ,  snake_case__     :     str=6     ,  snake_case__     :     List[str]=2_0_4_8     ,  snake_case__     :     Any=8     ,  snake_case__     :     List[str]=6     ,  snake_case__     :     Tuple=2_0_4_8     ,  snake_case__     :     Any=8     ,  snake_case__     :     str=0.0     ,  snake_case__     :     str=0.0     ,  snake_case__     :     List[str]=True     ,  snake_case__     :     Optional[int]="relu"     ,  snake_case__     :     str=2_5_6     ,  snake_case__     :     Tuple=0.1     ,  snake_case__     :     int=0.0     ,  snake_case__     :     Optional[Any]=0.0     ,  snake_case__     :     List[Any]=0.02     ,  snake_case__     :     Union[str, Any]=1.0     ,  snake_case__     :     Any=False     ,  snake_case__     :     Any="sine"     ,  snake_case__     :     Optional[int]="resnet50"     ,  snake_case__     :     Optional[Any]=True     ,  snake_case__     :     Any=False     ,  snake_case__     :     Optional[Any]=1     ,  snake_case__     :     Union[str, Any]=5     ,  snake_case__     :     str=2     ,  snake_case__     :     int=1     ,  snake_case__     :     Optional[int]=1     ,  snake_case__     :     List[str]=5     ,  snake_case__     :     List[str]=2     ,  snake_case__     :     Optional[Any]=0.1     ,  **snake_case__     :     List[str]     ,  )      ->	int:
				if backbone_config is not None and use_timm_backbone:
						raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'		)

				if not use_timm_backbone:
						if backbone_config is None:
								logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'		)
								_lowerCamelCase				   =  CONFIG_MAPPING['resnet'](out_features=['stage4']		)
						elif isinstance(snake_case__     ,  snake_case__		):
								_lowerCamelCase				   =  backbone_config.get('model_type'		)
								_lowerCamelCase				   =  CONFIG_MAPPING[backbone_model_type]
								_lowerCamelCase				   =  config_class.from_dict(snake_case__		)
						# set timm attributes to None
						_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  None, None, None

				_lowerCamelCase				   =  use_timm_backbone
				_lowerCamelCase				   =  backbone_config
				_lowerCamelCase				   =  num_channels
				_lowerCamelCase				   =  num_queries
				_lowerCamelCase				   =  d_model
				_lowerCamelCase				   =  encoder_ffn_dim
				_lowerCamelCase				   =  encoder_layers
				_lowerCamelCase				   =  encoder_attention_heads
				_lowerCamelCase				   =  decoder_ffn_dim
				_lowerCamelCase				   =  decoder_layers
				_lowerCamelCase				   =  decoder_attention_heads
				_lowerCamelCase				   =  dropout
				_lowerCamelCase				   =  attention_dropout
				_lowerCamelCase				   =  activation_dropout
				_lowerCamelCase				   =  activation_function
				_lowerCamelCase				   =  init_std
				_lowerCamelCase				   =  init_xavier_std
				_lowerCamelCase				   =  encoder_layerdrop
				_lowerCamelCase				   =  decoder_layerdrop
				_lowerCamelCase				   =  encoder_layers
				_lowerCamelCase				   =  auxiliary_loss
				_lowerCamelCase				   =  position_embedding_type
				_lowerCamelCase				   =  backbone
				_lowerCamelCase				   =  use_pretrained_backbone
				_lowerCamelCase				   =  dilation
				# Hungarian matcher
				_lowerCamelCase				   =  class_cost
				_lowerCamelCase				   =  bbox_cost
				_lowerCamelCase				   =  giou_cost
				# Loss coefficients
				_lowerCamelCase				   =  mask_loss_coefficient
				_lowerCamelCase				   =  dice_loss_coefficient
				_lowerCamelCase				   =  bbox_loss_coefficient
				_lowerCamelCase				   =  giou_loss_coefficient
				_lowerCamelCase				   =  eos_coefficient
				super().__init__(is_encoder_decoder=snake_case__     ,  **snake_case__		)
		@property
		def   _snake_case       (       self     :     Dict		)      ->	int:
				return self.encoder_attention_heads



		@property
		def   _snake_case       (       self     :     int		)      ->	int:
				return self.d_model





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		lowerCAmelCase_     =   version.parse('1.11'			)
		@property
		def   _snake_case       (       self     :     List[str]		)      ->	Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
				        ('pixel_mask', {0: 'batch'}),
				    ]		)
		@property
		def   _snake_case       (       self     :     Union[str, Any]		)      ->	float:
				return 1e-5



		@property
		def   _snake_case       (       self     :     List[Any]		)      ->	int:
				return 1_2
from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
				A						=			input('Enter image url: ').strip()
				print(F'''Downloading image from {url} ...''')
				A						=			BeautifulSoup(requests.get(url).content, 'html.parser')
				# The image URL is in the content field of the first meta tag with property og:image
				A						=			soup.find('meta', {'property': 'og:image'})['content']
				A						=			requests.get(image_url).content
				A						=			F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
				with open(file_name, 'wb') as fp:
								fp.write(image_data)
				print(F'''Done. Image saved to disk as {file_name}.''')